Hyperspectral Image Vegetation Change Detection Based on Biochemical Parameters Inversion

  • Qingyan WangEmail author
  • Junping Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)


Change detection of remote sensing images is a technology that one can get the change information by observing images of the same place obtained at different times. Hyperspectral remote sensing images can record detailed spectral information and reflect subtle differences between target and background. Hyperspectral change detection methods focus on changes between the different categories of feature, without fully taking the changes within the single ground type into account. In this paper, a hyperspectral vegetation change detection method based on biochemical parameters inversion is proposed. The change can be extracted from the vegetation biochemical parameters image by analyzing leaf water content, lignin content and other biochemical parameters. Experiments are conducted on both airborne and ground-based observation data. It shows that the change detection method based on biochemical parameters inversion reaches a high detection rate of 87.5% with a low false detection rate, which demonstrates superiority of the change detection methodology we proposed compared to other traditional methods.

Index Terms

Hyperspectral remote sensing image Vegetation biochemical parameters Vegetation index Change detection Component analysis 


  1. 1.
    Li, D.R.: Change detection from remote sensing images. Editorial Board of Geomatics and Information Science of Wuhan University, no. S1, pp. 7–12 (2003)Google Scholar
  2. 2.
    Petit, C., Scudder, T., Lambin, E.: Quantifying processes of land-cover change by remote sensing: resettlement and rapid land-cover changes in south-eastern Zambia. Int. J. Remote Sens. 22(17), 3435–3456 (2001)Google Scholar
  3. 3.
    Wang, L.W., Wei, Y.X., Niu, Z.: Analysis of vegetation dynamics over liaoning province based on remote sensing data. Spectrosc. Spectr. Anal. 12(5), 2956–2960 (2008)Google Scholar
  4. 4.
    Salvatore, R., Nicola, A., Marco, D., Giovanni, C., Thomas, O., Trym, V.H.: Detection of small changes in airborne hyperspectral imagery: experimental results over urban areas. In: 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, pp. 5–8 (2011)Google Scholar
  5. 5.
    Benedek, C., Sziranyi, T.: Change detection in optical aerial images by a multilayer conditional mixed markov model. IEEE Trans. Geosci. Remote Sens. 47(10), 3416–3430 (2009)Google Scholar
  6. 6.
    Celik, T.: Change detection in satellite images using a genetic algorithm approach. Geosci. Remote Sens. Lett. IEEE 7(2), 386–390 (2010)Google Scholar
  7. 7.
    Hsiuhan, L.Y., Melba, M.C.: Spectral and spatial proximity-based manifold alignment for multitemporal hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 54(1), 51–64 (2016)Google Scholar
  8. 8.
    Celik, T., Ma, K.K.: Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Trans. Geosci. Remote Sens. 49(2), 706–716 (2011)Google Scholar
  9. 9.
    Baodong, M., Ao, X., Song, Z., Lixin, W.: Retrieval of leaf water content for maize seedlings in visible near infrared and thermal infrared spectra. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 6930–6933 (2016)Google Scholar
  10. 10.
    Li, S.S., Yan, J.P., Wan, J.: The spatial-temporal changes of vegetation restoration on loess plateau in shaanxi-gansu-ningxia region. Acta Geogr. Sinica 67(7), 960–970 (2012)Google Scholar
  11. 11.
    Jiaoyang, H., Yehui, Q., Caili, G., Liyun, Z., et al.: Monitoring leaf area index after heading stage using hyperspectral remote sensing data in rice. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 6284–6287 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina

Personalised recommendations